A primer on reproducible research: Basic principles and common tools

Ben Marwick, University of Washington
December 2014

Overview

  • Definitions, motives, spectrum
  • Current practices
  • What I’m doing to improve reproducibility
  • What can be done to improve reproducibility?

Definitions are in flux

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Computational - Statistical - Emprirical
Stodden, V., et al. 2013. “Setting the default to reproducible.” computational science research. SIAM News 46: 4-6.

Motivations: Claerbout's principle

“An article about a computational result is advertising, not scholarship. The actual scholarship is the full software environment, code and data, that produced the result.” - Claerbout and Karrenbach, Proceedings of the 62nd Annual International Meeting of the Society of Exploration Geophysics. 1992

Claerbout's principle in detail

“When we publish articles containing figures which were generated by computer, we also publish the complete software environment which generates the figures” - Buckheit & Donoho, Wavelab and Reproducible Research, 1995.

“The scholarship does not only consist of theorems and proofs but also (and perhaps even more important) of data, computer code and a runtime environment which provides readers with the possibility to reproduce all tables and figures in an article.” - Hothorn et al., 2009 Case studies in reproducibility

Benefits are straightforward

  • Verification & Reliability: Easier to find and fix bugs. The results you produce today will be the same results you will produce tomorrow.
  • Transparency: Leads to broader impact, increased citation count, improved institutional memory
  • Efficiency: Reuse allows for de-duplication of effort. Payoff in the (not so) long run
  • Flexibility: When you don’t 'point-and-click' you gain many new analytic options.

But the limitations are substantial

Technical

  • Classified/sensitive/big data
  • Software licensing issues
  • Competition
  • Neither necessary nor sufficient for correctness (but essential for dispute resolution)

Cultural & personal

  • Reproducibility is not rewarded
  • No-one expects or requires reproducibility
  • No uniform standards of reproducibility, so no established user base
  • Embarassment

Our work exists on a spectrum of reproducibility

alt text Peng 2011, Science 334(6060) pp. 1226-1227

Goal is to expose the reader to more of the research workflow

alt text http://www.stodden.net/AMP2011/slides/pengslides.pdf

Current practices in many disciplines

  • Enter data in Excel
  • Use Excel for data cleaning & descriptive statistics
  • Import data into SPSS/SAS/Stata for further analysis
  • Use point-and-click options to run statistical analyses
  • Copy & paste output to Word document, repeatedly

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Click-trails compromise clarity

  • Lots of human effort for tedious & time-wasting tasks
  • Error-prone due to manual & ad hoc data handling (column and row offsets are common)
  • Difficult to record - hard to reconstruct a 'click history'
  • Tiny changes in data or method require extensive reworking efforts

Scripted analyses support scientific integrity

  • Plain text files will be readable for a long time
  • Improved transparency, automation, maintanability, accessibility, portability, efficiency, communicability of process (what more could we want?)
  • But there's a steep learning curve

What am I doing to encourage sharing?

Using literate statistical programming

Using an open source programming language

Using an open document formatting language

Using version control

Using dynamic documents

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Using convenient tools, services & support

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What can be done to improve code & data sharing?

alt text Stodden (IASSIST 2010) sampled American academics registered at the Machine Learning conference NIPS (134 responses from 593 requests (23%). Red = communitarian norms, Blue = private incentives

alt text Stodden (IASSIST 2010) sampled American academics registered at the Machine Learning conference NIPS (134 responses from 593 requests (23%). Red = communitarian norms, Blue = private incentives

Speed up culture change with incremental steps

  • Promote culture change through positive attribution
  • Implement mechanisms to indicate & encourage degrees of compliance (ie. clear definitions for different levels of reproducibility), cf. Stodden's:
    • 'Reproducible': compendium of text-code-data online
    • 'Reproduced': compendium available and independently reproduced
    • 'Semi-Reproducible': when the full compendium is not released
    • 'Semi-Reproduced': independent reproduction with other data

Promote existing standards to normalise reproducible research

  • Schwab et al.: ER (Easily reproducible), CR (Conditionally reproducible), NR (Not reproducible)
  • Biostatistics opt-in kite-marking of articles (Peng 2009): D (data), C (code), R (both)
  • Reproducible Research Standard (Stodden 2009): we should
    • Release the full compendium on the internet
    • License media such as text, figures, tables with Creative Commons Attribution license (CC-BY)
    • License code with one of Apache 2.0, MIT, LGPL, BSD, etc.
    • License “selection and arrangement” of data with CC0 or CC-BY

Use the Center for Open Science's badges

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An incentive to share data and code by acknowledging open practices with badges in publications. Currently used by Psychological Science

Integrate these values into everyday tasks

  • Train students by putting homework, assignments & dissertations on the reproducible research spectrum
  • Publish examples of reproducible research in our field
  • Request code & data when reviewing
  • Submit to & review for journals that support reproducible research
  • Critically review & audit data management plans in grant proposals
  • Consider reproducibility wherever possible in hiring, promotion & reference letters.

Thanks.

“Abandoning the habit of secrecy in favor of process transparency and peer review was the crucial step by which alchemy became chemistry.”

-Raymond, E. S., 2004, The art of UNIX programming: Addison-Wesley.

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